QKAN is a quantum algorithmic framework using block-encodings and QSVT to implement wide-and-shallow networks for quantum learning and compositional state preparation.
Ortiz Marrero, M
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Quantum state evolution in variational algorithms is governed by geometric phase rather than dynamical phase, with entanglement decoupled from evolution in hardware-efficient ansatzes but acting as a dynamical resource in Hamiltonian variational ansatzes.
A tunable mixing parameter p in random quantum circuits controls the transition from classically simulable to expressive quantum reservoir dynamics via entanglement and nonstabilizer content.
IQFMs iteratively constructs deep quantum feature maps from shallow circuits via classical augmentation weights and contrastive layer-wise training, outperforming QCNNs on noisy quantum data and matching classical neural networks on image classification without variational parameter optimization.
citing papers explorer
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QKAN: quantum Kolmogorov-Arnold networks with applications in machine learning and multivariate state preparation
QKAN is a quantum algorithmic framework using block-encodings and QSVT to implement wide-and-shallow networks for quantum learning and compositional state preparation.
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Calibrating the Role of Entanglement in Variational Quantum Algorithms from a Geometric Perspective
Quantum state evolution in variational algorithms is governed by geometric phase rather than dynamical phase, with entanglement decoupled from evolution in hardware-efficient ansatzes but acting as a dynamical resource in Hamiltonian variational ansatzes.
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Optimal quantum reservoir learning in proximity to universality
A tunable mixing parameter p in random quantum circuits controls the transition from classically simulable to expressive quantum reservoir dynamics via entanglement and nonstabilizer content.
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Iterative Quantum Feature Maps
IQFMs iteratively constructs deep quantum feature maps from shallow circuits via classical augmentation weights and contrastive layer-wise training, outperforming QCNNs on noisy quantum data and matching classical neural networks on image classification without variational parameter optimization.